Robust recognition of printed Chinese characters using multilayer perceptron and Walsh functions
Kou‐Yuan Huang, Hsiang-Tsun Yen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
In this paper, a neural network approach for the recognition of printed Chinese characters is developed. A multi-layer perceptron is trained as the classifier by using the back-propagation algorithm, and the Walsh functions are employed for feature extraction. Thirty similar Chinese characters (classes) are designed in the experimental domain. The network is initially trained with noisefree training samples, and is retrained gradually with misclassified noisy testing patterns to improve the robustness of the classifier. Through classifying a large set of 9000 unknown testing patterns of various noise degrees, a great augmentation in system robustness and an encouraging recognition performance are presented.